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live-wire是一种常用于医学图像的交互式分割方法,其基本思想是利用动态规划方法产生图像中给定两点间的最优路径,合理地构造代价函数和选择起始点和目标点,用以提取物体的边缘,十分有效,该份文件中包含说明文件,操作方便.-live-wire is a common medical images in the interactive segmentation method, The basic idea is to use dynamic programming methods to image
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MICCAI2008脑影像分割文章，包括Interactive Liver Tumor Segmentation using Graph-cut and watershed等-Brain image segmentation MICCAI2008 articles, including the Interactive Liver Tumor Segmentation using Graph-cut and watershed, etc.
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Graph Cuts是一个图像分割方法，具有交互式分割，分割效果很好。-Graph Cuts is an image segmentation method, with interactive segmentation, segmentation works well.
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In this paper, we describe the development of a rapidly reconfigurable system in which the users’ tacit knowledge and requirements are
elicited via a process of Interactive Evolution, finding the image processing parameters to achieve the required
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实现了二维图像的种子填充算法，该算法可以用来被用来实现脑图像的交互式分割-Using vc++ implementation of the two-dimensional image of the seed filling algorithm, which can be used be used to achieve interactive segmentation of brain images
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GrowCut algorithm from "GrowCut" - Interactive Multi-Label N-D Image Segmentation：可用于目标提取的分割算法，包括算法原文
-GrowCut algorithm from " GrowCut" - Interactive Multi-Label ND Image Segmentation: segmentation can be used for object extraction algori
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This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them. It discusses the “Grab-Cut” technique, and reviews some some of the common matting techniques. The graph cu
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实现了三维超声PNN算法的GPU加速；实现GPU加速的CT圆轨道平板探测器FDK重建算法；提供对canny边缘检测算法的支持；提供交互式graph cut分割方法；实现OSTU分割算法；-Three dimensional ultrasound PNN algorithm GPU acceleration to achieve GPU accelerated CT reconstruction algorithm for circular orbits FDK flat panel detec
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实现了区域合并的图像分割的经典方法，是Interactive Image Segmentation by Maximal Similarity based Region Merging 论文的源码，可以好好研究-Regional integration to achieve the classic method of image segmentation is the Interactive Image Segmentation by Maximal Similarity based Regio
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选取读入一份彩色图像，在运行Untitled.m程序时：先用鼠标交互的选取一个颜色区域作为你要与其他色彩分割的目标，，鼠标选取好后，按ENTER健后出现进行分割出现结果。
-Select a color image read in the run Untitled.m program: the first interaction with the mouse, select a color region as the color you want to split with other go
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基于FCM和图割的交互式图像分割方法，关于图割的应用-FCM and the graph cuts based interactive image segmentation method applied on the graph cuts
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彩色图像分割，灰常不错。This function implements kmeans clustering on an input RGB (m x n x 3)
image. The user inputs at least two inputs: IMGIN and NCLUSTERS, and this
function will step through an interactive color segmentation using kmeans clustering. It
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1.GeometricContext文件是完成图片中几何方向目标分类。
参考文献《Automatic Photo Pop-up》Hoiem 2005
2 GrabCut文件是完成图像中目标交互式分割
参考文献《“GrabCut” — Interactive Foreground Extraction using Iterated Graph Cuts》
C. Rother 2004
3 HOG文件是自己编写的根据HOG特征检测行人的matlab代码
4 虹膜识别程序
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一个用于图像分割的程序，需要事先的人工标定，很有意思。-One for the image segmentation process that requires manual calibration in advance, very interesting.
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基于图像区域生长的方法之一 交互式分割法-Image-based region growing methods: interactive segmentation method
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SegmentIt: Watershed Based Image Segmentation Tool. This interactive segmentation tool allows to switch back and forth between the watershed approaches (watershedSegmentIt: Watershed Based Image Segmentation Tool.
-SegmentIt: Watershed Based Image
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本文介绍了一种结合live wire算法和活动轮廓模型的医学图像序列的分割方法.我们通过把live wire
算法和图像分割中一般的区域增长方法结合来改进live wire算法,并用改进后的算法来对医学图像序列中的单张或
多张切片进行交互式的准确分割.然后计算机利用活动轮廓模型来自动分割相邻的未分割切片.我们通过在活动轮廓
模型的边缘点中引入记录已分割物体边缘附近局部区域特征的灰度模型来把已分割切片中的物体与背景的局部区域
特征带入相邻的未分割切片中,并用由灰度模型定义的区域相似
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GrabCut 算法是一种高效的前景背景分割算法。传统分割算法应用纹理或边界信息中的一种进行分割, 而GrabCut 算法
综合利用了纹理及边界两种信息进行图像分割。GrabCut 算法是对Graph cuts 算法的改进, 具有分割精度高, 交互式操作少的优
点, 并将分割从灰度图像推广到了彩色图像领域, 是一种比较有前途的交互式分割算法。该算法用在静态图像编辑中的图片背景
更换, 取得了很好的效果。-The GrabCut algorithm is an efficient pr
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交互式图像分割英文论文数篇，学习交互式图像分割十分有用。-Several English dissertations on interactive image segmentation, useful for learning interactive image segmentation.
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Tae Hoon Kim教授的Nonparametric Higher-Order Learning for Interactive Segmentation源码-Tae Hoon Kim, Professor Nonparametric Higher-Order Learning for Interactive Segmentation
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